Exploring satellite based temporal forecast modelling of Aedes aegypti oviposition from an operational perspective

Germán, Alba; Espinosa, Manuel; Abril, Marcelo; Scavuzzo, Carlos M.

doi:10.1016/j.rsase.2018.07.006

Cited by 14 publications

(35 citation statements)

References 31 publications

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“…The mosquitoes' collection was implemented by ovitraps, classical dipping techniques [41,42] or by recording indices, like the Breteau Index (BI) [33,43], House Index (HI) or Container Index (CI) [33,43,44], and the Entomological Inoculation Rate (EIR) [45,46] indices, the latter being a commonly used measure that estimates the number of infected bites per person and per unit time (usually year) [47]. Different kinds of traps (e.g., light traps, magnet traps baited with octenol, CO 2 baited traps, odour-baited MM-X) were used by the various studies [41,45,46,[48][49][50][51][52] as a method to capture mosquitoes, and produce the vectors' density assessments and its status as infected or not. It is worth noting that there is only one study [53] that used crowdsourced data, providing information on both the vector (% of mosquito bites, % of mosquito Larvae) and human cases (% of known human dengue cases) in the area.…”

Section: State-of-the-art Reviewmentioning

confidence: 99%

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

et al. 2019

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Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

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Section: State-of-the-art Reviewmentioning

confidence: 99%

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

et al. 2019

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“…Remote sensing (RS) is a promising tool in epidemiological studies (German, et al 2018, Misslin and Daudé 2017, Buczak, et al 2014, Araujo, et al 2015. This study used the Tropical Rainfall Measurement Mission (TRMM) product 3B43 to obtain the monthly average rainfall.…”

Section: Climatic Factorsmentioning

confidence: 99%

“…The utilization of RS in our study made it possible to access spatiotemporal temperature, precipitation, and vegetation data for each village. These types of data overcome the limited accessibility of such information at fine scales in areas where the spatial coverage of weather stations is coarse (German, et al 2018). By using Google Earth Pro, we were able to use cloud computation to conduct all preliminary processing of the data, which significantly shortened the working time.…”

Section: Accessibility Of Data Modeling Approach and Limitationsmentioning

confidence: 99%

“…However, many studies that utilize LU based on satellite images contain certain limitations. In this type of map, built-up areas are often treated as a single category (Vanwambeke, et al 2006, Ibarra, et al 2014, German, et al 2018, preventing the ability to further distinguish the subcategories of land utilization such as residential, commercial, industrial, etc. These different categories of LU have different ecological responses to mosquito and dengue dynamics (Thammapalo, et al 2007); hence, detailed maps might amplify the chances to capture fine scale variations of mosquito habitats and dengue incidence.…”

Section: Introductionmentioning

confidence: 99%

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Dengue Disease Dynamics are Modulated by the Combined Influence of Precipitation and Landscapes: A Machine Learning-based Approach

Francisco

Carvajal

Ryo

et al. 2020

Preprint

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BackgroundDengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors interact in time and space to affect mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain or flood water.ObjectivesThis study aimed to investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence.MethodsEntomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metro Manila, Philippines, from 2012 to 2014. Temperature, precipitation, and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterwards, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combinatory influences of landscape and climate factors on the ovitrap index and dengue incidence.ResultsThe MOB recursive partitioning for the ovitrap index indicated that mosquito occurrence was higher in high residential density areas, where industrial areas also exist and are well connected with roads. Precipitation was another key covariate modulating the effects of landscape factors, possibly by expanding breeding sites and activating mosquito reproduction. Moreover, the MOB recursive partitioning indicated that precipitation was the main predictor of dengue incidence, with a stronger effect in high residential density and commercial areas.DiscussionPrecipitation with floods has epidemiologically important implications by damaging shelters and causing population displacement, thus increasing exposure to dengue vectors. Our findings suggest that the intensification of vector control during the rainy season can be prioritized in residential and commercial areas to better control dengue disease dynamics.

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“…The Ae. aegypti species is known to be adaptable to urban environments due to its inclination to being bred in artificial containers [19,21,27,28]. Spread of the causal diseases by this vector is known to correlate with the local adult vector population [29].…”

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mosquitoes.pdf

Mudele¹,

Bayer²,

Zanandrez³

et al. 2019

Preprint

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Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most A PREPRINT -NOVEMBER 26, 2019 informative ones, a more parsimonious but equally effective and explainable model can be obtained. Moreover, the results can be empirically interpreted for use in vector control activities.Keywords Ae. aegypti · Machine learning · Random forest · Remote sensing IntroductionStudying urban ecosystems is a hot topic within various scientific clusters [1][2][3][4][5]. The compounding effects of human activities through urbanization, carbon emission and other biodiversity changes are modifying urban ecosystems, thus creating a need for continuous assessment of the environment to ensure health and quality of life suitability for dwellers [6]. With a repository covering over 40 years of data collected, Earth observation (EO) data from orbiting satellites present a gold mine for environmental change monitoring [7]. Due to recent advances resulting in higher resolution sensors (spectral, spatial and temporal), freely accessible data, and efficient processing algorithms, it is possible to extract static and dynamic phenomena happening on the Earth surface and use this information for various environmental characterization procedures [8][9][10][11].One majo...

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Exploring satellite based temporal forecast modelling of Aedes aegypti oviposition from an operational perspective

Cited by 14 publications

References 31 publications

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

Dengue Disease Dynamics are Modulated by the Combined Influence of Precipitation and Landscapes: A Machine Learning-based Approach

mosquitoes.pdf

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